Crash Injury Severity Analysis Using Bayesian Ordered Probit Models

Conjoint analysis is used to evaluate consumer preferences for three consumer-ready products derived from crawfish. Utility functions are estimated using two-limit tobit and ordered probit models. The results show women prefer a baked nugget or popper type product, whereas 35- to 44-year-old men prefer a microwavable nugget or patty type product. The results also show little difference between part-worth estimates or predicted rankings for the tobit and ordered probit models, implying the results are not sensitive to assumptions regarding the ordinal and cardinal nature of respondent preferences.

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Evaluating the predicting power of ordered probit models for multiple business cycle phases in the U.S. and Japan

Journal of the Japanese and International Economies ◽

10.1016/j.jjie.2018.08.002 ◽

2018 ◽

Vol 50 ◽

pp. 60-71 ◽

Cited By ~ 1

Author(s):

Christian R. Proaño ◽

Artur Tarassow

Keyword(s):

Business Cycle ◽

Ordered Probit ◽

Probit Models ◽

Ordered Probit Models ◽

The U.S

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Consumer interest in information cues denoting quality, traceability and origin: An application of ordered probit models to beef labels

Food Quality and Preference ◽

10.1016/j.foodqual.2005.05.010 ◽

2006 ◽

Vol 17 (6) ◽

pp. 453-467 ◽

Cited By ~ 209

Author(s):

Wim Verbeke ◽

Ronald W. Ward

Keyword(s):

Ordered Probit ◽

Consumer Interest ◽

Probit Models ◽

Information Cues ◽

Ordered Probit Models

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An analysis of motorcycle injury and vehicle damage severity using ordered probit models

Journal of Safety Research ◽

10.1016/s0022-4375(02)00051-8 ◽

2002 ◽

Vol 33 (4) ◽

pp. 445-462 ◽

Cited By ~ 197

Author(s):

Mohammed A. Quddus ◽

Robert B. Noland ◽

Hoong Chor Chin

Keyword(s):

Ordered Probit ◽

Probit Models ◽

Ordered Probit Models

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On the importance of the probabilistic model in identifying the most decisive games in a tournament

Journal of Quantitative Analysis in Sports ◽

10.1515/jqas-2016-0013 ◽

2017 ◽

Vol 13 (1) ◽

Cited By ~ 1

Author(s):

Francisco Corona ◽

Juan de Dios Tena Horrillo ◽

Michael Peter Wiper

Keyword(s):

Statistical Approach ◽

Decision Makers ◽

Ordered Probit ◽

World Cup ◽

Fifa World Cup ◽

Probit Models ◽

Great Relevance ◽

Ordered Probit Models ◽

Original Measure

AbstractIdentifying the decisive matches in international football tournaments is of great relevance for a variety of decision makers such as organizers, team coaches and/or media managers. This paper addresses this issue by analyzing the role of the statistical approach used to estimate the outcome of the game on the identification of decisive matches on international tournaments for national football teams. We extend the measure of decisiveness proposed by Geenens (2014) in order to allow us to predict or evaluate the decisive matches before, during and after a particular game on the tournament. Using information from the 2014 FIFA World Cup, our results suggest that Poisson and kernel regressions significantly outperform the forecasts of ordered probit models. Moreover, we find that although the identification of the most decisive matches is independent of the model considered, the identification of other key matches is model dependent. We also apply this methodology to identify the favorite teams and to predict the most decisive matches in 2015 Copa America before the start of the competition. Furthermore, we compare our forecast approach with respect to the original measure during the knockout stage.

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